AI in hedge funds: analyzing proposed problems (Pt. 1)

By this point, the exponential growth of artificial intelligence (AI) within investing has coerced many into an unstoppable — albeit potentially slow — adaptation process. Autonomous technologies have established new framework in terms of industry potential, and they could very well revolutionize hedge funding and asset managing as we know it.

However, these exciting projections have come with their share of cautionary inverses. Resistors of AI still cite a series of potential problems that have kept them hesitant — especially within quantitative hedge funds, where high risk scenarios are already a common part of the process. Here, now, is a closer look at a few of these proposed issues.

Unneeded (and ineffective) advancement?

According to the technology review, there is a chance “AI could make markets more volatile right when they lease need it.” This claim is reinforced by reports suggesting that, as a result of a lack of necessary data, the technology may lack the ability to make key presumptions about financial stability, and it could possibly act unfavorably in the event of another major recession or similar crisis. If these suggestions prove to be true, AI’s increased presence might amount to an unnecessary novelty doing little to reinforce firms for the future.

Many lingering AI fears may very well stem from these analyses, and it is easy to see why; after all, financial representation via pseudo-sentient software is as scary as it is profound. However, it is likely too early to make broad claims of AI’s perceived detriments on the industry. The impact of machine learning and deep learning have become incredibly far-reaching, proving to be useful in numerous investing facets.

Trade challenges

An increasing amount of hedge funds, new and established, have gained confidence in the autonomous handling of important trade decisions, shaking up old paradigms by trusting bots with their customers’ money. This approach has come with obstacles, but these issues have challenged AI proponents to think innovatively about finance and trade. The most frequent debate, in this regard, boils down to a classic volley of autonomous error vs. human folly — essentially, is human oversight still the dominate means of mitigating problems?

Expanding on this concept, some critics of AI suggest the technology will be unable to handle “mysterious parameters” influencing financial markets, such as politics, news events, and unexpected incidents like natural disasters. Nevertheless, when tracing the unprecedented growth of AI and ML potential in just the last few years alone, it is also logical to assume AI algorithms will one day grow smart enough to identify, absorb, and contextualize such variables in their handling of trade data.